Deep Learning is a new area of research that is getting us closer in achieving one of the primary objectives of Machine Learning – Artificial Intelligence.
It is used widely in the fields of Image Recognition, Natural Language Processing (NLP) and Video Classification.
Deep Learning Conf is a single day conference followed by workshops on the second day. The conference will have full, crisp and lightning talks from morning to evening. The workshops on the next day will introduce participants to neural networks followed by two tracks of three-hour workshops on NLP and Computer Vision / AI. Participants can join either one of the two workshop tracks.
We are looking for talks and workshops from academics and practitioners of Deep Learning on the following topics:
- Applications of Deep Learning in software.
- Applications of Deep Learning in hardware.
- Conceptual talks and cutting edge research on Deep Learning.
- Building businesses with Deep Learning at the core.
We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Lightning talks of 5 mins duration.
Selection process #
Proposals will be filtered and shortlisted by an Editorial Panel. Along with your proposal, you must share the following details:
- Links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience.
- Blog posts you may have written related to your proposal.
- Outline of your proposed talk – either in the form of a mind map or a text document or draft slides.
If your proposal involves speaking about a library / tool / software that you intend to open source in future, the proposal will be considered only when the library / tool / software in question is made open source.
We will notify you about the status of your proposal within two-three weeks of submission.
Selected speakers have to participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you prepare for speaking at the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
Commitment to open source #
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
Key dates and deadlines #
- Proposal submission deadline: 31 May 2016
- Schedule announcement: 15 June 2016
- Conference dates: 1 July 2016
CMR Institute of Technology, Bangalore
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Practical Deep Learning
This session will equip users with knowledge on Deep Learning. At end of session audience should have sufficient knowledge of deep learning networks, where they can be applied and what are the benefits of using the same. They will also get some practical tips on implementing these algorithms. An overview of how we build a a Text classifier using deep learning approach will be given. Results obtained on same will be show cased. Our lessons learnt while working with deep learning networks will be discussed.
This session will cover What is Deep Learning, What can you do with deep Learning, What is it’s relationship with Machine Learning, Brief introduction to the working of an Artificial neural network, Introduction to a Deep Learning Algorithm (Long Short Term Memory Networks ), Availabe frameworks for deep learning, An overview of how we built a working text classification system using LSTM in python, Tips based on experience with deep learning networks
Basic understanding of machine learning concepts can help to better appreciate the presentation though this is not mandatory.
Speaker bio #
Arthi Venkataraman has 19+ years of experience in the design, development and testing of projects in different domains • She is currently a Senior Member in the Distinguished Members of Technical Staff cadre at Wipro Technologies • Her current role involves solution development for different business problems spanning the area of Natural Language Processing, Machine Learning and Semantics Technologies
She has a B.E Degree in Computer Science from University Visvesvariah College of Engineering, Bangalore and an MBA (PGDSM) from IIM, Bangalore.
She has previously presented papers and spoken at other international conferences This presentation is based on Arthi’s experience in area of building a large scale production grade classifier using Python at her organization.
- Conferences Spoken At.
- Spoke at Grace Hopper 2015 - https://www.youtube.com/channel/UCGuAMvPJ0l9xscCfoxUpEDA
- Spoke at Pycon 2015 - (https://in.pycon.org/2015/ )
- Spoke at Fifth Elephant 2013 https://fifthelephant.talkfunnel.com/2013/644-similar-entity-detection-in-large-data
- Papers Published.